
Deep Reinforcement Learning (RL) is a powerful general-purpose framework for learning behavior policies from high-dimensional interaction data, and has led to a multitude of impressive feats in application areas such as game-playing [1]} and robotics [2]}, [3]}. Through interaction with an unknown environment, RL agents iteratively improve their policy by learning to maximize a reward signal, which has the potential to be used in lieu of hand-crafted control policies. However, the performance of policies learned by RL is found to be highly dependent on the careful specification of task-specific reward functions and, as a result, crafting a good reward function may require significant domain knowledge and technical expertise.
